Forschungsgebiete der Theoretischen Physik 2

Wir untersuchen dynamische Instabilitäten, die durch  op­tische Rückkopplung, optische Injektion oder Moden­kopp­lung in Lasern  entstehen. Die Ladungsträgerdynamik im Verstärkermaterial (z.B. Halbleiter-Nanostrukturen) spielt dabei eine zentrale Rolle. Ein weiteres Forschungsthema betrifft die Frage, inwieweit diese optischen Systeme für Hardware-basiertes  maschinelles Lernen eingesetzt werden können. Wir verwenden numerische Methoden zur Lösung von gekoppelten Differentialgleichungen sowie analytische Methoden der Nichtlineare Dynamik für die Bifurkationsanalyse.

Forschungsschwerkpunkte Prof. Dr. Kathy Lüdge

EU-Projekt SPIKEPro

Brain-inspired or neuromorphic chips working with biologically inspired spiking neural networks have gained attention as they promise highly efficient ways to process data. Developing neuromorphic systems with electronic and photonic hardware is part of the new European collaborative project SPIKEPro (Spiking Photonic-Electronic IC for Quick and Efficient Processing) within the European Innovation Council (EIC) framework. TU Ilmenau is one of the partners that are gathered from TU Eindhoven, University of Strathclyde, University College London, and HP Enterprise Belgium. SPIKEPro proposes a science-towards-technology breakthrough by combining low-energy electrical and photonic neurons into a joint spiking neural network on an integrated circuit. SPIKEPro’s chip integration approach is based on a common technology platform, connecting ultrafast laser optical neurons with efficient electrical spiking diodes through non-volatile synaptic weights. This enables to simultaneously capitalise on the advantages of both electronics and photonics to deliver efficient and high-speed SNNs going beyond existing implementations. In addition to reducing the energy consumption per spike in the network, SPIKEPro will also develop novel learning strategies and algorithms able to work with reduced number of synaptic connections. The outcome of SPIKEPro will have lasting economic, societal and scientific impact. The project will bring ultra-fast and efficient neuromorphic hardware into the disparate fields of edge computing, sensor data processing, high-speed control and computational neuroscience.

Reservoir Computing ist eine leicht in Hardware realisierbare Methode des maschinellen Lernens und wird in der Arbeitsgruppe intensiv untersucht. Im von der Carl-Zeiss Stiftung geförderten Projekt NeurosensEar Neuromorphe akustische Sensorik für leistungsfähige Hörgeräte von morgen untersuchen wir micro-mechanische Resonatoren als InSensor Reservoir Computer.

Reservoir Computing for in-sensor applications
Neuromorphic computing with optics
Bild1_DPG_ProjektThP2_70Prozent.pngKathy Lüdge

DFG Projekt (2020-2023)
"Hybrid photonic computing in delay-coupled non-linear systems with memory"

Teilprojekt im Sonderforschungsbereich SFB910 (2019-2022)
"Collective phenomena in laser networks with nonidentical units"

  • realize all optical reservoir computing schemes (evaluation via benchmark tasks: Chaotic time series prediction, Memory capacity, Channel equalization etc.)
  • develop numerical framework for simulating highly connected delay-coupled networks with memory,
  • analyze impact of network topology on computing performance.
  • explore correlations of performance and bifurcation structure
Nonlinear laser dynamics
  • modeling semiconductor quantum-dot lasers with optical feedback, injection or network-coupling
  • emission stability of two-state lasing devices, optical switching applications, neuronal spiking
  • complex emission dynamics of nano- and micro-lasers
Theoretische Physik 2
Frequency combs and short pulse generation
Bild3_ML_jitter_2delayThP2.jpgTheoretische Physik 2
  • pulse shaping in passively mode-locked lasers
  • timing-jitter calculations and performance tuning via optical feedback
  • coupled mode-locked lasers